Health literacy in pregnant women facing prenatal screening may explain their intention to use a patient decision aid: a short report
Bibliographic record
Abstract
BACKGROUND: It has been suggested that health literacy may impact the use of decision aids (DAs) among patients facing difficult decisions. Embedded in the pilot test of a questionnaire, this study aimed to measure the association between health literacy and pregnant women's intention to use a DA to decide about prenatal screening. We recruited a convenience sample of 45 pregnant women in three clinical sites (family practice teaching unit, birthing center and obstetrical ambulatory care clinic). We asked participating women to complete a self-administered questionnaire assessing their intention to use a DA to decide about prenatal screening and assessed their health literacy levels using one subjective and two objective scales. RESULTS: Two of the three scales discriminated between levels of health literacy (three numeracy questions and three health literacy questions). We found a positive correlation between pregnant women's intention to use a DA and subjective health literacy (Spearman coefficient, Rho 0.32, P = 0.04) but not objective health literacy (Spearman coefficient, Rho 0.07, P = 0.65). Hence subjective health literacy may affect the intention to use a DA among pregnant women facing a decision about prenatal screening. CONCLUSION: Special attention should be given to pregnant women with lower health literacy levels to increase their intention to use a DA and ensure that every pregnant women can give informed and value-based consent to prenatal screening.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".